Profile Triton Inference Server

Triton Inference Server (TIS) is another serving method supported by LMDeploy besides api_server. Its performance testing methods and metrics are similar to those of api_server.

The profiling script is profile_serving.py. Before running it, please install the lmdeploy precompiled package, download the profiling script and the test dataset:

pip install 'lmdeploy[serve]'
git clone --depth=1 https://github.com/InternLM/lmdeploy
cd lmdeploy/benchmark
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

Metrics

LMDeploy records the performance metrics like first token latency, token throughput (tokens/s) and request throughput (RPM)

first_token_latency is only reported in the case of streaming inference.

The formula for calculating token throughput is:

$$ TokenThroughput=Number\ of\ generated\ tokens/TotalTime $$

And the formula for calculating request throughput is:

$$ RPM(request\ per\ minute)=Number\ of\ prompts/TotalTime * 60 $$

Total time includes prefill time.

Profile

In this section, we take internlm/internlm-7b as an example to show the benchmark procedure.

Launch triton inference server

Before launching the server, the LLM model must be converted to the turbomind format in advance.

lmdeploy convert internlm internlm/internlm-7b --dst-path ./internlm-7b --trust-remote-code

Then, the triton inference server can be launched by:

bash ./internlm-7b/service_docker_up.sh

Profile

python3 profile_serving.py 0.0.0.0:33337 ./internlm-7b/triton_models/tokenizer ./ShareGPT_V3_unfiltered_cleaned_split.json

For detailed argument specification of profile_serving.py, such as request concurrency, sampling parameters an so on, please run the help command python3 profile_serving.py -h.